Urban rail transit demand analysis and prediction: A review of recent studies

Zhiyan Fang; Qixiu Cheng; Ruo Jia; Zhiyuan Liu

doi:10.1007/978-3-319-92231-7_31

Urban rail transit demand analysis and prediction: A review of recent studies

Zhiyan Fang
, Qixiu Cheng
, Ruo Jia
, Zhiyuan Liu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

8 Citations (Scopus)

Abstract

Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.

Original language	English
Title of host publication	Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference
Editors	Robert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain, Giuseppe De Pietro, Luigi Gallo, Lakhmi C. Jain, Ljubo Vlacic, Robert J. Howlett
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	300-309
Number of pages	10
ISBN (Print)	9783319922300
DOIs	https://doi.org/10.1007/978-3-319-92231-7_31
Publication status	Published - Jun 2018
Externally published	Yes
Event	11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018 - Gold Coast, Australia Duration: 20 Jun 2018 → 22 Jun 2018

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	98
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018
Country/Territory	Australia
City	Gold Coast
Period	20/06/18 → 22/06/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

ASJC Scopus subject areas

General Decision Sciences
General Computer Science

More information

10.1007/978-3-319-92231-7_31

Cite this

Fang, Z., Cheng, Q., Jia, R., & Liu, Z. (2018). Urban rail transit demand analysis and prediction: A review of recent studies. In R. J. Howlett, L. C. Jain, L. C. Jain, G. De Pietro, L. Gallo, L. C. Jain, L. Vlacic, & R. J. Howlett (Eds.), Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference (pp. 300-309). (Smart Innovation, Systems and Technologies; Vol. 98). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-92231-7_31

Fang, Zhiyan ; Cheng, Qixiu ; Jia, Ruo et al. / Urban rail transit demand analysis and prediction : A review of recent studies. Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference. editor / Robert J. Howlett ; Lakhmi C. Jain ; Lakhmi C. Jain ; Giuseppe De Pietro ; Luigi Gallo ; Lakhmi C. Jain ; Ljubo Vlacic ; Robert J. Howlett. Springer Science and Business Media Deutschland GmbH, 2018. pp. 300-309 (Smart Innovation, Systems and Technologies).

@inproceedings{37c23666a43740f3a8e91bbf7577613f,

title = "Urban rail transit demand analysis and prediction: A review of recent studies",

abstract = "Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.",

author = "Zhiyan Fang and Qixiu Cheng and Ruo Jia and Zhiyuan Liu",

note = "Funding Information: Acknowledgement. This study is supported by the General Projects (No. 71771050) and Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603). Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2019.; 11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018 ; Conference date: 20-06-2018 Through 22-06-2018",

year = "2018",

month = jun,

doi = "10.1007/978-3-319-92231-7\_31",

language = "English",

isbn = "9783319922300",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "300--309",

editor = "Howlett, \{Robert J.\} and Jain, \{Lakhmi C.\} and Jain, \{Lakhmi C.\} and \{De Pietro\}, Giuseppe and Luigi Gallo and Jain, \{Lakhmi C.\} and Ljubo Vlacic and Howlett, \{Robert J.\}",

booktitle = "Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference",

address = "Germany",

}

Fang, Z, Cheng, Q, Jia, R & Liu, Z 2018, Urban rail transit demand analysis and prediction: A review of recent studies. in RJ Howlett, LC Jain, LC Jain, G De Pietro, L Gallo, LC Jain, L Vlacic & RJ Howlett (eds), Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference. Smart Innovation, Systems and Technologies, vol. 98, Springer Science and Business Media Deutschland GmbH, pp. 300-309, 11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018, Gold Coast, Australia, 20/06/18. https://doi.org/10.1007/978-3-319-92231-7_31

Urban rail transit demand analysis and prediction: A review of recent studies. / Fang, Zhiyan; Cheng, Qixiu; Jia, Ruo et al.
Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference. ed. / Robert J. Howlett; Lakhmi C. Jain; Lakhmi C. Jain; Giuseppe De Pietro; Luigi Gallo; Lakhmi C. Jain; Ljubo Vlacic; Robert J. Howlett. Springer Science and Business Media Deutschland GmbH, 2018. p. 300-309 (Smart Innovation, Systems and Technologies; Vol. 98).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N1 - Funding Information: Acknowledgement. This study is supported by the General Projects (No. 71771050) and Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603). Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2019.

PY - 2018/6

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AB - Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.

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Fang Z, Cheng Q, Jia R, Liu Z. Urban rail transit demand analysis and prediction: A review of recent studies. In Howlett RJ, Jain LC, Jain LC, De Pietro G, Gallo L, Jain LC, Vlacic L, Howlett RJ, editors, Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference. Springer Science and Business Media Deutschland GmbH. 2018. p. 300-309. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-3-319-92231-7_31

Urban rail transit demand analysis and prediction: A review of recent studies

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

More information

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